The Prognostic Value of Integrated Analysis of Inflammation and Hypoxia-Related Genes in Idiopathic Pulmonary Fibrosis

HE4-based nomogram for predicting overall survival in patients with idiopathic pulmonary fibrosis: construction and validation

Idiopathic pulmonary fibrosis (IPF) is a life-threatening interstitial lung disease. Identifying biomarkers for early diagnosis is of great clinical importance. The epididymis protein 4 (HE4) is important in the process of inflammation and fibrosis in the epididymis. Its prognostic value in IPF, however, has not been studied. The mRNA and protein levels of HE4 were used to determine the prognostic value in different patient cohorts. In this study, prognostic nomograms were generated based on the results of the cox regression analysis. We identified the HE4 protein level increased in IPF patients, but not the HE4 gene expression. The increased expression of HE4 correlated positively with a poor prognosis for patients with IPF. The HR and 95% CI were 2.62 (1.61-4.24) (p < 0.001) in the training set. We constructed a model based on the risk-score = 0.16222182 * HE4 + 0/0.37580659/1.05003609 (for GAP index 0-3/4-5/6-8) + (- 1.1183375). In both training and validation sets, high-risk patients had poor prognoses (HR: 3.49, 95%CI 2.10-5.80, p = 0.001) and higher likelihood of dying (HR: 6.00, 95%CI 2.04-17.67, p = 0.001). Analyses of calibration curves and decision curves suggest that the method is effective in predicting outcomes. Furthermore, a similar formulation was used in a protein-based model based on HE4 that also showed prognostic value when applied to IPF patients. Accordingly, HE4 is an independent poor prognosis factor, and it has the potential to predict IPF patient survival.

Consensus Gene Co-Expression Network Analysis Identifies Novel Genes Associated with Severity of Fibrotic Lung Disease

Idiopathic pulmonary fibrosis (IPF) is a severe fibrotic lung disease characterized by irreversible scarring of the lung parenchyma leading to dyspnea, progressive decline in lung function, and respiratory failure. We analyzed lung transcriptomic data from independent IPF cohorts using weighted gene co-expression network analysis (WGCNA) to identify gene modules based on their preservation status in these cohorts. The consensus gene modules were characterized by leveraging existing clinical and molecular data such as lung function, biological processes, pathways, and lung cell types. From a total of 32 consensus gene modules identified, two modules were found to be significantly correlated with the disease, lung function, and preserved in other IPF datasets. The upregulated gene module was enriched for extracellular matrix, collagen metabolic process, and BMP signaling while the downregulated module consisted of genes associated with tube morphogenesis, blood vessel development, and cell migration. Using a combination of connectivity-based and trait-based significance measures, we identified and prioritized 103 "hub" genes (including 25 secretory candidate biomarkers) by their similarity to known IPF genetic markers. Our validation studies demonstrate the dysregulated expression of CRABP2, a retinol-binding protein, in multiple lung cells of IPF, and its correlation with the decline in lung function.